This map shows the geographic impact of Lewis Smith's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lewis Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lewis Smith more than expected).
This network shows the impact of papers produced by Lewis Smith. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lewis Smith. The network helps show where Lewis Smith may publish in the future.
Co-authorship network of co-authors of Lewis Smith
This figure shows the co-authorship network connecting the top 25 collaborators of Lewis Smith.
A scholar is included among the top collaborators of Lewis Smith based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Lewis Smith. Lewis Smith is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Amersfoort, Joost van, et al.. (2021). Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression..7 indexed citations
5.
Farquhar, Sebastian, Lewis Smith, & Yarin Gal. (2020). Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations. arXiv (Cornell University). 33. 4346–4357.1 indexed citations
6.
Amersfoort, Joost van, Lewis Smith, Yee Whye Teh, & Yarin Gal. (2020). Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network. arXiv (Cornell University). 1.9 indexed citations
7.
Amersfoort, Joost van, Lewis Smith, Yee Whye Teh, & Yarin Gal. (2020). Uncertainty Estimation Using a Single Deep Deterministic Neural Network. International Conference on Machine Learning. 9690–9700.14 indexed citations
8.
Farquhar, Sebastian, Lewis Smith, & Yarin Gal. (2020). Try Depth Instead of Weight Correlations: Mean-field is a Less Restrictive Assumption for Deeper Networks.. arXiv (Cornell University).2 indexed citations
Smith, Lewis & Yarin Gal. (2018). Understanding Measures of Uncertainty for Adversarial Example Detection. Uncertainty in Artificial Intelligence. 560–569.12 indexed citations
Tay, Guan K., Campbell S. Witt, Frank Christiansen, et al.. (1995). Matching for MHC haplotypes results in improved survival following unrelated bone marrow transplantation.. PubMed. 15(3). 381–5.51 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.